# Fine-grained Emotion and Intent Learning in Movie Dialogues

arXiv: https://arxiv.org/abs/2012.13624 · HTML: https://mlsanity.com/paper/2012.13624/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking Dataset](https://mlsanity.com/paper/2407.02751/) (2024)
- [A Unified Framework for Emotion Identification and Generation in Dialogues](https://mlsanity.com/paper/2205.15513/) (2022)
- [SayNext-Bench: Why Do LLMs Struggle with Next-Utterance Anticipation?](https://mlsanity.com/paper/2602.00327/) (2026)
- [Use of a Taxonomy of Empathetic Response Intents to Control and Interpret Empathy in Neural Chatbots](https://mlsanity.com/paper/2305.10096/) (2023)
- [EmplifAI: a Fine-grained Dataset for Japanese Empathetic Medical Dialogues in 28 Emotion Labels](https://mlsanity.com/paper/2601.10033/) (2026)

## Abstract (arXiv)

We propose a novel large-scale emotional dialogue dataset, consisting of 1M dialogues retrieved from the OpenSubtitles corpus and annotated with 32 emotions and 9 empathetic response intents using a BERT-based fine-grained dialogue emotion classifier. This work explains the complex pipeline used to preprocess movie subtitles and select good movie dialogues to annotate. We also describe the semi-supervised learning process followed to train a fine-grained emotion classifier to annotate these dialogues. Despite the large set of labels, our dialogue emotion classifier achieved an accuracy of 65% and was used to annotate 1M emotional movie dialogues from OpenSubtitles. This scale of emotional dialogue classification has never been attempted before, both in terms of dataset size and fine-grained emotion and intent categories. Visualization techniques used to analyze the quality of the resultant dataset suggest that it conforms to the patterns of human social interaction.